System, Method, and Computer Program Product for Trajectory Scoring During an Autonomous Driving Operation Implemented with Constraint Independent Margins to Actors in the Roadway

ABSTRACT

Provided are autonomous vehicles (AV), computer program products, and methods for maneuvering an AV in a roadway, including receiving forecast information associated with predicted trajectories of one or more actors in a roadway, determining a relevant trajectory of an actor based on correlating a forecast for predicted trajectories of the actor with the trajectory of the AV, regenerate a distance table for the relevant trajectory previously generated for processing constraints, generate a plurality of margins for the AV to evaluate, the margins based on a plurality of margin types for providing information about risks and effects on passenger comfort associated with a future proximity of the AV to the actor, classifying an interaction between the AV and the actor based on a plurality of margins, and generating continuous scores for each candidate trajectory that is also within the margin of the actor generated for the relevant trajectory.

BACKGROUND Field

This disclosure relates generally to topological planning and, in somenon-limiting embodiments or aspects, to systems, methods, and computerprogram products for generating scores in a continuous manner andevaluating the scores for the margins to other relevant actors in acomputationally efficient manner, by determining scores for candidatetrajectories based on dynamic margins between an autonomous vehicle (AV)and the predicted trajectories of actors in the same roadway or actorsentering the same roadway.

Description of Related Art

The AV is required to find an optimal road/lane-level path from the AV'scurrent location to a specified destination (e.g., a goal position,etc.) in a map including a road network. To travel autonomously mayrequire a route; however, navigating a route may involve the creationand evaluation of at least one trajectory or path through the roadnetwork and may require evaluating any number of potential lane changes(for example, the AV may merge into a leftmost lane to make a left turn,perform lane changes to overtake a slower object, etc.), as well as,in-lane maneuvers (for example, tracking behind an object, stoppingbefore a stationary object, steering around a stationary object, etc.).

Creating a trajectory to handle lane changes to account for constraints(e.g., objects in the roadway, other cars, pedestrians, bus stops, traincrossings, etc.) in an environment of the AV may involve processing andstoring a vast amount of information defining the roadway in theelectronic memory of a vehicle computing device and calculating a costfunction for each of a plurality of constraints in the environment ofthe surrounding roadway, up to a destination of the vehicle. Suchcalculations may also take into account a state of the vehicle thatincludes a location and an orientation of the vehicle and the dynamiccapabilities of the AV. Only then may an obstacle-free trajectory bedetermined for the AV from its current location to the goal position byminimizing a calculated cost and comparing one or more costs associatedwith each obstacle-free trajectory.

For performing lane changes, the AV may perform calculations todetermine a number of possible candidate trajectories, which may beinfinitely expensive in terms of computing cost (e.g., computationallyinfeasible, etc.) because of the number of variations of transitionstart/end locations, transition start/end times, steering/speedprofiles, and/or the like. In some navigating conditions, such as, densetraffic, to successfully complete a lane change may require the AV tomake a complex maneuver, for example, to speed up and steer abruptlyinto a narrow gap between vehicles in the destination lane that iscurrently several vehicles ahead. In existing autonomous driving systemsthat only consider a finite number of lane change variations, it is notpossible to strategically select the variations explored in order toplan a successful lane change maneuver in these conditions.

Similarly, for in-lane maneuvers, systems may only sample a finitenumber of candidate trajectories in the road network, either bydiscretization or random sampling. Because the number of sampledtrajectories is limited, this approach may fail to find a feasibletrajectory when one exists, or it may find a suboptimal trajectory,because feasible or optimal trajectories are not sampled.

SUMMARY

Accordingly, disclosed are improved computer-implemented systems,methods, and computer program products for trajectory scoring during anautonomous driving operation implemented with constraint independentmargins to actors in the roadway.

According to non-limiting embodiments or aspects, provided is acomputer-implemented method of maneuvering an autonomous vehicle (AV)traversing a roadway, comprising: receiving, by one or more processorsof a vehicle computing system, forecast information associated with oneor more predicted trajectories of one or more actors in the roadway;regenerating, by the one or more processors, a distance table for arelevant trajectory of an action, using an original distance table whichwas previously generated for processing constraints, the relevanttrajectory being based on a forecast for one or more predictedtrajectories of the actor with the trajectory of the AV; generating, bythe one or more processors, a plurality of margins for the AV toevaluate, the plurality of margins being based on a plurality of margintypes for providing information about risks and effects on passengercomfort associated with a future proximity of the AV to the actor;generating, by the one or more processors, continuous scores for eachcandidate trajectory that is also within the margin of the actorgenerated for the relevant trajectory; and issuing, by the one or moreprocessors, a command to control the AV based on the continuous scores,to maneuver with respect to a plurality of constraints in the roadway.

In some non-limiting embodiments or aspects, the computer-implementedmethod further includes that the plurality of margins include at leastone of a longitudinal margin or a lateral margin and generating theplurality of margins further comprises: generating a temporallongitudinal margin associated with a relevant time interval based ontime remaining until the AV reaches the actor, generating the temporallongitudinal margin, including for the relevant time interval:determining a position of the AV on a reference path and the nearesttime interval where a location of a longitudinal interval of the actoroverlaps with that of the position; and generating a spatiallongitudinal margin providing information for determining proximity ofthe actor when moving at low speeds where the temporal longitudinalmargins may be large despite the AV having a small spatial margin to theactor, generating the spatial longitudinal margin includes for eachrelevant time interval: determining lateral distance table entries ofthe actor and determining a location interval of the reference path overwhich the actor is deemed within a threshold of the candidate trajectoryand considered in the reference path of the AV; and generating a lateralmargin distance as the minimum lateral offset value over a longitudinallocation interval of the AV for the time interval being evaluated.

In some non-limiting embodiments or aspects, the computer-implementedmethod further includes that classifying an interaction between the AVand the actor, wherein the classifying the interaction comprises: inresponse to predicting a collision, performing an interactionclassification to identify a time step associated with an instance ofthe collision, wherein the interaction classification identifies thetime step in a time sequence of a collision time interval that a firstpolygon intersection is found, wherein the first polygon intersectionidentifies the collision time interval based on a first longitudinalmargin of a plurality of longitudinal margins that indicates acollision; and otherwise, for each time interval, performing aninteraction classification to identify a spatial longitudinal marginaccounted to be the minimum spatial longitudinal margin in the locationinterval of the reference path when the actor is predicted to be at aminimum distance from the candidate trajectory of the AV's path, oralternatively, identifying the time interval over the longitudinallocation interval of the actor when it is predicted to have a minimumlateral offset value from the candidate trajectory of the AV.

In some non-limiting embodiments or aspects, the computer-implementedmethod further includes that the computer-implemented method of claim 1,wherein a previous AV-side of the actor comprises at least one of:ahead-of-AV; behind-AV; unknown; or undetermined.

In some non-limiting embodiments or aspects, the computer-implementedmethod further comprises, iterating over time intervals to propagateinformation to each interval along the trajectory; and generating anAV-side of the actor at each time interval based on a distance margin,and one or more parameters for scoring longitudinal margins that canchange based on the AV-side of the actor.

In some non-limiting embodiments or aspects, the computer-implementedmethod further comprises identifying whether a spatial gap existsincluding at least one time interval since a previous time intervalwhich, when traversed by the actor was not included in a reference pathof the AV, and predicting, based on the time interval, whether the AV ispredicted to move through the actor, or alternatively, beside or aroundthe actor, based on when a distance interval switches from ahead of tobehind the AV, or alternatively, behind the AV to ahead of the AV, themethod comprising: setting a previous AV-side of the actor to be unknownif a spatial gap has been identified and there has not been a collision;setting the AV-side of the actor to be unaltered at a time intervalbased on a longitudinal distance margin and detecting a collision when alongitudinal distance margin is equal to zero; detecting a collision hasoccurred when a current AV-side of the actor has been altered and a gapin the interval is not found; and setting a current AV-side of the actoras the previous AV-side of the actor when a collision has occurred.

In some non-limiting embodiments or aspects, the computer-implementedmethod further includes that further comprises a backward propagation ofa plurality of longitudinal margins from a first point of intersectionbetween the actor and the AV, wherein backward propagation is determinedby iterating backward over each time interval and setting an AV-side ofthe actor if it is unknown for a current time interval, using a previoustime interval.

In some non-limiting embodiments or aspects, the computer-implementedmethod further includes that providing interaction classificationcomprises at least one of: providing scoring margins; configuring toremove uncertainty; incorporating right of way; or incorporatingcollision severity.

In some non-limiting embodiments or aspects, the computer-implementedmethod further includes that a probability of a collision is based onone of: 1) a probability of a prediction adjusted to consider a right ofway and reactivity, or 2) a predicted probability of compliance and anability to adjust course; and wherein a severity of an anticipatedcollision is based on one of: 1) a relative speed of the AV and theactor, 2) an actor classification comprising one of: a vulnerable roaduser; a vehicle; an animal; a static actor; or a background actor, or 3)a point of impact on the AV, on the actor, or both.

In some non-limiting embodiments or aspects, the computer-implementedmethod further includes that generating continuous scores for eachcandidate trajectory is based on one or more of: a penalty configured bytransforming a margin value at each time interval into a penalty; adiscount factor to account for increasing uncertainty about interactionsthat will be performed further in the future, wherein each subsequenttime interval is offset from a start of a planning cycle; an actor-typefactor, discounting parameters per actor type, including at least one ofa vehicle, a crosswalk-following pedestrian, other pedestrian, cyclist,or other object, to apply based on interaction classifications; or asocial acceptability factor that classifies a decision taken by acandidate trajectory with respect to the margins to a plurality ofactors simultaneously.

According to non-limiting embodiments or aspects, provided is anautonomous vehicle or autonomous vehicle compute system, comprising oneor more processors; and a memory that stores one or more instructionsthat, when executed by the one or more processors, cause the one or moreprocessors to: receive forecast information associated with one or morepredicted trajectories of one or more actors in the roadway; regeneratea distance table for the relevant trajectory of an action, using anoriginal distance table which was previously generated for processingconstraints, the relevant trajectory being based on a forecast for oneor more predicted trajectories of the actor with the trajectory of theAV; generate a plurality of margins for the AV to evaluate, theplurality of margins being based on a plurality of margin types forproviding information about risks and effects on passenger comfortassociated with a future proximity of the AV to the actor; generatecontinuous scores for each candidate trajectory that is also within themargin of the actor generated for the relevant trajectory; and issue acommand to control the AV based on the continuous scores, to maneuverwith respect to a plurality of constraints in the roadway.

According to non-limiting embodiments or aspects, a computer programproduct for cloud and autonomous vehicle compute testing, comprising atleast one non-transitory computer-readable medium including one or moreinstructions that, when executed by at least one processor, cause theone or more processors to: generate a temporal longitudinal marginassociated with a relevant time interval based on time remaining untilthe AV reaches the actor, generating the temporal longitudinal marginincludes for the relevant time interval, determining a position of theAV on a reference path and the nearest time interval where a location ofa longitudinal interval of the actor overlaps with that of the position;generate a spatial longitudinal margin providing information fordetermining proximity of the actor when moving at low speeds where thetemporal longitudinal margins may be large despite the AV having a smallspatial margin to the actor, generating the spatial longitudinal marginincludes for each relevant time interval: determine lateral distancetable entries of the actor and determining a location interval of thereference path over which the actor is deemed within a threshold of thecandidate trajectory and considered in the reference path of the AV; andgenerate a lateral margin distance as the minimum lateral offset valueover a longitudinal location interval of the AV for the time intervalbeing evaluated.

Further non-limiting embodiments or aspects are set forth in thefollowing numbered clauses:

Clause 1: A computer-implemented method of maneuvering an autonomousvehicle (AV) traversing a roadway, comprising: receiving, by one or moreprocessors of a vehicle computing system, forecast informationassociated with one or more predicted trajectories of one or more actorsin the roadway; regenerating, by the one or more processors, a distancetable for a relevant trajectory of an action, using an original distancetable which was previously generated for processing constraints, therelevant trajectory being based on a forecast for one or more predictedtrajectories of the actor with the trajectory of the AV; generating, bythe one or more processors, a plurality of margins for the AV toevaluate, the plurality of margins being based on a plurality of margintypes for providing information about risks and effects on passengercomfort associated with a future proximity of the AV to the actor;generating, by the one or more processors, continuous scores for eachcandidate trajectory that is also within the margin of the actorgenerated for the relevant trajectory; and issuing, by the one or moreprocessors, a command to control the AV based on the continuous scores,to maneuver with respect to a plurality of constraints in the roadway.

Clause 2: The computer-implemented method of clause 1, wherein theplurality of margins include at least one of a longitudinal margin or alateral margin and generating the plurality of margins furthercomprises: generating a temporal longitudinal margin associated with arelevant time interval based on time remaining until the AV reaches theactor, generating the temporal longitudinal margin, including for therelevant time interval: determining a position of the AV on a referencepath and the nearest time interval where a location of a longitudinalinterval of the actor overlaps with that of the position; and generatinga spatial longitudinal margin providing information for determiningproximity of the actor when moving at low speeds where the temporallongitudinal margins may be large despite the AV having a small spatialmargin to the actor, generating the spatial longitudinal margin includesfor each relevant time interval: determining lateral distance tableentries of the actor and determining a location interval of thereference path over which the actor is deemed within a threshold of thecandidate trajectory and considered in the reference path of the AV; andgenerating a lateral margin distance as the minimum lateral offset valueover a longitudinal location interval of the AV for the time intervalbeing evaluated.

Clause 3: The computer-implemented method of clauses 1-2, furthercomprising classifying an interaction between the AV and the actor,wherein the classifying the interaction comprises: in response topredicting a collision, performing an interaction classification toidentify a time step associated with an instance of the collision,wherein the interaction classification identifies the time step in atime sequence of a collision time interval that a first polygonintersection is found, wherein the first polygon intersection identifiesthe collision time interval based on a first longitudinal margin of aplurality of longitudinal margins that indicates a collision; andotherwise, for each time interval, performing an interactionclassification to identify a spatial longitudinal margin accounted to bethe minimum spatial longitudinal margin in the location interval of thereference path when the actor is predicted to be at a minimum distancefrom the candidate trajectory of the AV's path, or alternatively,identifying the time interval over the longitudinal location interval ofthe actor when it is predicted to have a minimum lateral offset valuefrom the candidate trajectory of the AV.

Clause 4: The computer-implemented method of clauses 1-3, wherein aprevious AV-side of the actor comprises at least one of: ahead-of-AV;behind-AV; unknown; or undetermined.

Clause 5: The computer-implemented method of clauses 1-4, furthercomprising: iterating over time intervals to propagate information toeach interval along the trajectory; and generating an AV-side of theactor at each time interval based on a distance margin, and one or moreparameters for scoring longitudinal margins that can change based on theAV-side of the actor.

Clause 6: The computer-implemented method of clauses 1-5, furthercomprising: identifying whether a spatial gap exists including at leastone time interval since a previous time interval which, when traversedby the actor was not included in a reference path of the AV, andpredicting, based on the time interval, whether the AV is predicted tomove through the actor, or alternatively, beside or around the actor,based on when a distance interval switches from ahead of to behind theAV, or alternatively, behind the AV to ahead of the AV, the methodcomprising: setting a previous AV-side of the actor to be unknown if aspatial gap has been identified and there has not been a collision;setting the AV-side of the actor to be unaltered at a time intervalbased on a longitudinal distance margin and detecting a collision when alongitudinal distance margin is equal to zero; detecting a collision hasoccurred when a current AV-side of the actor has been altered and a gapin the interval is not found; and setting a current AV-side of the actoras the previous AV-side of the actor when a collision has occurred.

Clause 7: The computer-implemented method of clauses 1-6, furthercomprising a backward propagation of a plurality of longitudinal marginsfrom a first point of intersection between the actor and the AV, whereinbackward propagation is determined by iterating backward over each timeinterval and setting an AV-side of the actor if it is unknown for acurrent time interval, using a previous time interval.

Clause 8: The computer-implemented method of clauses 1-7, whereinproviding interaction classification comprises at least one of:providing scoring margins; configuring to remove uncertainty;incorporating right of way; or incorporating collision severity.

Clause 9: The computer-implemented method of clauses 1-8, wherein aprobability of a collision is based on one of: 1) a probability of aprediction adjusted to consider a right of way and reactivity, or 2) apredicted probability of compliance and an ability to adjust course; andwherein a severity of an anticipated collision is based on one of: 1) arelative speed of the AV and the actor, 2) an actor classificationcomprising one of: a vulnerable road user; a vehicle; an animal; astatic actor; or a background actor, or 3) a point of impact on the AV,on the actor, or both.

Clause 10: The computer-implemented method of clauses 1-9, whereingenerating continuous scores for each candidate trajectory is based onone or more of: a penalty configured by transforming a margin value ateach time interval into a penalty; a discount factor to account forincreasing uncertainty about interactions that will be performed furtherin the future, wherein each subsequent time interval is offset from astart of a planning cycle; an actor-type factor, discounting parametersper actor type, including at least one of a vehicle, acrosswalk-following pedestrian, other pedestrian, cyclist, or otherobject, to apply based on interaction classifications; or a socialacceptability factor that classifies a decision taken by a candidatetrajectory with respect to the margins to a plurality of actorssimultaneously.

Clause 11: A system for maneuvering an autonomous vehicle (AV)traversing a roadway, comprising: one or more processors; and a memorythat stores one or more instructions that, when executed by the one ormore processors, cause the one or more processors to: receive forecastinformation associated with one or more predicted trajectories of one ormore actors in the roadway; regenerate a distance table for the relevanttrajectory of an action, using an original distance table which waspreviously generated for processing constraints, the relevant trajectorybeing based on a forecast for one or more predicted trajectories of theactor with the trajectory of the AV; generate a plurality of margins forthe AV to evaluate, the plurality of margins being based on a pluralityof margin types for providing information about risks and effects onpassenger comfort associated with a future proximity of the AV to theactor; generate continuous scores for each candidate trajectory that isalso within the margin of the actor generated for the relevanttrajectory; and issue a command to control the AV based on thecontinuous scores, to maneuver with respect to a plurality ofconstraints in the roadway.

Clause 12: The system of clause 11, wherein the plurality of marginsinclude at least one of a longitudinal margin or a lateral margin, andthe one or more processors are further configured to: generate atemporal longitudinal margin associated with a relevant time intervalbased on time remaining until the AV reaches the actor, generating thetemporal longitudinal margin includes for the relevant time interval,determining a position of the AV on a reference path and the nearesttime interval where a location of a longitudinal interval of the actoroverlaps with that of the position; and generate a spatial longitudinalmargin providing information for determining proximity of the actor whenmoving at low speeds, where the temporal longitudinal margins may belarge despite the AV having a small spatial margin to the actor, andgenerating the spatial longitudinal margin, includes for each relevanttime interval, further comprising: generating lateral distance tableentries associated with the actor; generating a location interval of thereference path over which the actor is deemed within a threshold of thecandidate trajectory and considered in the reference path of the AV; andgenerating a lateral margin distance as the minimum lateral offset valueover a longitudinal location interval of the AV for the time intervalbeing evaluated.

Clause 13: The system of clauses 11-12, wherein the one or moreprocessors are further configured to classify an interaction between theAV and the actor to: perform, in response to predicting a collision, aninteraction classification to identify a time step associated with aninstance of the collision, wherein the interaction classificationidentifies the time step in a time sequence of a collision time intervalthat a first polygon intersection is found, wherein the first polygonintersection identifies the collision time interval based on a firstlongitudinal margin of a plurality of longitudinal margins thatindicates a collision; and otherwise, for each time interval, perform aninteraction classification to identify a spatial longitudinal marginaccounted to be the minimum spatial longitudinal margin in the locationinterval of the reference path when the actor is predicted to be theleast distance from the candidate trajectory of the AV's path, oralternatively, identify the time interval over the longitudinal locationinterval of the actor when it is predicted to have a minimum lateraloffset value from the candidate trajectory of the AV.

Clause 14: The system of clauses 11-13, wherein the one or moreprocessors are further configured to: identify whether a spatial gapexists including at least one time interval since a previous timeinterval which, when traversed by the actor, was not included in areference path of the AV, and predicting, based on the time interval,whether the AV is predicted to move through the actor, or alternatively,beside or around the actor, based on when a distance interval switchesfrom ahead of the AV to behind the AV or, alternatively, behind the AVto ahead of the AV, the system further comprising at least one of:setting a previous AV-side of the actor to be unknown if a spatial gaphas been identified and there has not been a collision; setting theAV-side of the actor to be unaltered at a time interval based on alongitudinal distance margin and detecting a collision when alongitudinal distance margin is equal to zero; detecting a collision hasoccurred when a current AV-side of the actor has been altered and a gapin the interval is not found; or setting a current AV-side of the actoras the previous AV-side of the actor when a collision has occurred.

Clause 15: The system of clauses 11-14, wherein the one or moreprocessors are further configured to backward propagate a plurality oflongitudinal margins from a first point of intersection between theactor and the AV, wherein backward propagation is determined byiterating backward over each time interval and setting an AV-side of theactor if it is unknown for a current time interval, using a previoustime interval.

Clause 16: The system of clauses 11-15, wherein providing interactionclassification comprises providing scoring margins, configured to removeuncertainty, incorporate right of way, or incorporate collisionseverity.

Clause 17: The system of clauses 11-16, wherein a probability of acollision is based on one of: 1) a probability of a prediction adjustedto consider a right of way and reactivity, or 2) a predicted probabilityof compliance and an ability to adjust course; and wherein a severity ofan anticipated collision is based on one of: 1) a relative speed of theAV and the actor, 2) an actor classification comprising one of: avulnerable road user; a vehicle; an animal; a static actor; or abackground actor, or 3) a point of impact on the AV, on the actor, orboth.

Clause 18: The system of clauses 11-17, wherein the scores for eachcandidate trajectory, are based on one or more of: a penalty configuredby transforming a margin value at each time interval into a penalty; adiscount factor to account for increasing uncertainty about interactionsthat will be performed further in the future, wherein each subsequenttime interval is offset from a start of a planning cycle; an actor-typefactor, discounting parameters per actor type, including at least one ofa vehicle, a crosswalk-following pedestrian, other pedestrian, cyclist,or other object, to apply based on interaction classifications; or asocial acceptability factor that classifies a decision taken by acandidate trajectory with respect to the margins to a plurality ofactors simultaneously.

Clause 19: A computer program product for maneuvering an autonomousvehicle (AV) traversing a route on a roadway, comprising at least onenon-transitory computer-readable medium including one or moreinstructions that, when executed by one or more processors, cause theone or more processors to: receive forecast information associated withone or more predicted trajectories of one or more actors in a roadway;regenerate a distance table for the relevant trajectory of an action,using an original distance table which was previously generated forprocessing constraints, the relevant trajectory being based on aforecast for one or more predicted trajectories of the actor with thetrajectory of the AV; generate a plurality of margins for the AV toevaluate, the margins based on a plurality of margin types for providinginformation about risks and effects on passenger comfort associated witha future proximity of the AV to the actor; generate continuous scoresfor each candidate trajectory that is also within the margin of theactor generated for the relevant trajectory; and issue a command tocontrol the AV based on the continuous scores, to maneuver with respectto a plurality of constraints in the roadway.

Clause 20: The computer program product of clause 19, wherein theplurality of margins include at least one of a longitudinal margin or alateral margin, and further including one or more instructions that,when executed by the one or more processors, cause the one or moreprocessors to: generate a temporal longitudinal margin associated with arelevant time interval based on time remaining until the AV reaches theactor, generate the temporal longitudinal margin includes for therelevant time interval, and determine a position of the AV on areference path and the nearest time interval where a location of alongitudinal interval of the actor overlaps with that of the position,further comprising; generating a spatial longitudinal margin providinginformation for determining proximity of the actor when moving at lowspeeds where the temporal longitudinal margins may be large despite theAV having a small spatial margin to the actor, generating the spatiallongitudinal margin includes for each relevant time interval; generatinglateral distance table entries of the actor and determine a locationinterval of the reference path over which the actor is deemed within athreshold of the candidate trajectory and considered in the referencepath of the AV; and generating a lateral margin distance as the minimumlateral offset value over a longitudinal location interval of the AV forthe time interval being evaluated.

These and other features and characteristics of the present disclosure,as well as, the methods of operation and functions of the relatedelements of structures and the combination of parts and economies ofmanufacture, will become more apparent upon consideration of thefollowing description and the appended claims with reference to theaccompanying drawings, all of which form a part of this specification,wherein like reference numerals designate corresponding parts in thevarious figures. It is to be expressly understood, however, that thedrawings are for the purpose of illustration and description only andare not intended as a definition of the limits of the presentdisclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

Additional advantages and details of the present disclosure areexplained in greater detail below with reference to the exemplaryembodiments that are illustrated in the accompanying schematic figures,in which:

FIGS. 1A-1B are diagrams of non-limiting embodiments or aspects of amargin scoring environment in which systems, apparatuses, and/ormethods, as described herein, may be implemented;

FIG. 2 is a diagram of non-limiting embodiments or aspects of anautonomous vehicle (AV) in which constraint independent scoring ofmargins and vehicle control, as described herein, may be implemented;

FIG. 3 is a flowchart illustrating a non-limiting embodiment or aspectof a method for generating continuous margin scores according to theprinciples of the present disclosure;

FIG. 4 is a diagram of non-limiting embodiments or aspects showingexamples of temporal longitudinal margins employed for scoringtrajectories of an AV according to the principles of the presentdisclosure;

FIG. 5 is a diagram of non-limiting embodiments or aspects showingexamples of spatial longitudinal margins employed for scoringtrajectories of an AV according to the principles of the presentdisclosure;

FIG. 6 is a diagram of non-limiting embodiments or aspects showingexamples of lateral margins employed for scoring trajectories of an AVaccording to the principles of the present disclosure;

FIG. 7 is a flowchart illustrating a non-limiting embodiment or aspectof a method for determining an AV-side of an actor and accounting forpost-collision margins according to the principles of the presentdisclosure; and

FIG. 8 is a flowchart illustrating a non-limiting embodiment or aspectof a method for classifying an interaction of an AV and an actor todetermine properties of a forecast risk according to the principles ofthe present disclosure.

DETAILED DESCRIPTION

For purposes of the description hereinafter, the terms “end,” “upper,”“lower,” “right,” “left,” “vertical,” “horizontal,” “top,” “bottom,”“lateral,” “longitudinal,” and derivatives thereof shall relate to thedisclosure as it is oriented in the drawing figures. However, it is tobe understood that the disclosure may assume various alternativevariations and step sequences, except where expressly specified to thecontrary. It is also to be understood that the specific devices andprocesses illustrated in the attached drawings, and described in thefollowing specification, are simply exemplary embodiments or aspects ofthe disclosure. Hence, specific dimensions and other physicalcharacteristics related to the embodiments or aspects of the embodimentsor aspects disclosed herein are not to be considered as limiting unlessotherwise indicated. In addition, terms of relative position, such as,“vertical” and “horizontal”, “ahead” and “behind”, or “front” and“rear”, when used, are intended to be relative to each other and neednot be absolute, and only refer to one possible position of the deviceassociated with those terms depending on the device's orientation.

No aspect, component, element, structure, act, step, function,instruction, and/or the like used herein should be construed as criticalor essential unless explicitly described as such. Also, as used herein,the articles “a” and “an” are intended to include one or more items, andmay be used interchangeably with “one or more” and “at least one.”Furthermore, as used herein, the term “set” is intended to include oneor more items (e.g., related items, unrelated items, a combination ofrelated and unrelated items, and/or the like) and may be usedinterchangeably with “one or more” or “at least one.” Where only oneitem is intended, the term “one” or similar language is used. Also, asused herein, the terms “has,” “have,” “having,” or the like are intendedto be open-ended terms. Further, the phrase “based on” is intended tomean “based at least partially on” unless explicitly stated otherwise.Additionally, when terms, such as, “first” and “second” are used tomodify a noun, such use is simply intended to distinguish one item fromanother, and is not intended to require a sequential order unlessspecifically stated.

In some non-limiting embodiments or aspects, one or more aspects may bedescribed herein, in connection with thresholds (e.g., a tolerance, atolerance threshold, etc.). As used herein, satisfying a threshold mayrefer to a value (e.g., a score, an objective score, etc.) being greaterthan the threshold, more than the threshold, higher than the threshold,greater than or equal to the threshold, less than the threshold, fewerthan the threshold, lower than the threshold, less than or equal to thethreshold, equal to the threshold, etc.

As used herein, the terms “communication” and “communicate” may refer tothe reception, receipt, transmission, transfer, provision, and/or thelike of information (e.g., data, signals, messages, instructions,commands, and/or the like). For one unit (e.g., a device, a system, acomponent of a device or system, combinations thereof, and/or the like)to be in communication with another unit means that the one unit is ableto directly or indirectly receive information from and/or send (e.g.,transmit) information to the other unit. This may refer to a direct orindirect connection that is wired and/or wireless in nature.Additionally, two units may be in communication with each other eventhough the information transmitted may be modified, processed, relayed,and/or routed between the first and second unit. For example, a firstunit may be in communication with a second unit even though the firstunit passively receives information and does not actively sendinformation to the second unit. As another example, a first unit may bein communication with a second unit if at least one intermediary unit(e.g., a third unit located between the first unit and the second unit)processes information received from the first unit and sends theprocessed information to the second unit. In some non-limitingembodiments or aspects, a message may refer to a network packet (e.g., adata packet and/or the like) that includes data.

As used herein, the term “computing device” may refer to one or moreelectronic devices configured to process data. A computing device may,in some examples, include the necessary components to receive, process,and output data, such as, a processor, a display, a memory, an inputdevice, a network interface, and/or the like. A computing device may beincluded in a device onboard an autonomous vehicle (AV). As an example,a computing device may include an onboard specialized computer (e.g., asensor, a controller, a data store, a communication interface, a displayinterface, etc.), a mobile device (e.g., a smartphone, standard cellularphone, or integrated cellular device), a portable computer, a wearabledevice (e.g., watches, glasses, lenses, clothing, and/or the like), apersonal digital assistant (PDA), and/or other like devices. A computingdevice may also be a desktop computer or other form of non-mobilecomputer.

As used herein, the terms “client” and “client device” may refer to oneor more computing devices that access a service made available by aserver. In some non-limiting embodiments or aspects, a “client device”may refer to one or more devices that facilitate a maneuver by an AV,such as, one or more remote devices communicating with an AV. In somenon-limiting embodiments or aspects, a client device may include acomputing device configured to communicate with one or more networksand/or facilitate vehicle movement, such as, but not limited to, one ormore vehicle computers, one or more mobile devices, and/or other likedevices.

As used herein, the term “server” may refer to or include one or morecomputing devices that are operated by or facilitate communication andprocessing for multiple parties in a network environment, such as, theInternet, although it will be appreciated that communication may befacilitated over one or more public or private network environments andthat various other arrangements are possible. Further, multiplecomputing devices (e.g., servers, data stores, controllers,communication interfaces, mobile devices, and/or the like) directly orindirectly communicating in the network environment may constitute a“system.” Reference to “a server” or “a processor,” as used herein, mayrefer to a previously-recited server and/or processor that is recited asperforming a previous step or function, a different server and/orprocessor, and/or a combination of servers and/or processors. Forexample, as used in the specification and the claims, a first serverand/or a first processor that is recited as performing a first step orfunction may refer to the same or different server and/or a processorrecited as performing a second step or function.

As used herein, the term “system” may refer to one or more computingdevices or combinations of computing devices, such as, but not limitedto, processors, servers, client devices, software applications, and/orother like components. In addition, reference to “a server” or “aprocessor,” as used herein, may refer to a previously-recited serverand/or processor that is recited as performing a previous step orfunction, a different server and/or processor, and/or a combination ofservers and/or processors. For example, as used in the specification andthe claims, a first server and/or a first processor that is recited asperforming a first step or function may refer to the same or differentserver and/or a processor recited as performing a second step orfunction.

As used herein, an “electronic device” or a “computing device” refers toa device that includes a processor and memory. Each device may have itsown processor and/or memory, or the processor and/or memory may beshared with other devices as in a virtual machine or containerarrangement. The memory will contain or receive programming instructionsthat, when executed by the processor, cause the electronic device toperform one or more operations according to the programminginstructions.

As used herein, the terms “memory,” “memory device,” “data store,” “datastorage facility,” and the like each refer to a non-transitory device onwhich computer-readable data, programming instructions, or both arestored. Except where specifically stated otherwise, the terms “memory,”“memory device,” “data store,” “data storage facility,” and the like areintended to include single device embodiments, embodiments in whichmultiple memory devices together or collectively store a set of data orinstructions, as well as, individual sectors within such devices.

As used herein, the terms “processor” and “processing device” refer to ahardware component of an electronic device that is configured to executeprogramming instructions. Except where specifically stated otherwise,the singular term “processor” or “processing device” is intended toinclude both single-processing device embodiments and embodiments inwhich multiple processing devices together or collectively perform aprocess.

As used herein, the term “vehicle” refers to any moving form ofconveyance that is capable of carrying either one or more humanoccupants and/or cargo and is powered by any form of energy. The term“vehicle” includes, but is not limited to, cars, trucks, vans, trains,autonomous vehicles, aircraft, aerial drones, and the like. An“autonomous vehicle” (AV) is a vehicle having a processor, programminginstructions and drivetrain components that are controllable by theprocessor without requiring a human operator. An AV may be fullyautonomous in that it does not require a human operator for most or alldriving conditions and functions, or it may be semi-autonomous in that ahuman operator may be required in certain conditions or for certainoperations, or that a human operator may override the vehicle'sautonomous system and may take control of the vehicle. The AV can be aground-based AV (e.g., car, truck, bus, etc.), an air-based AV (e.g.,airplane, drone, helicopter, or other aircraft), or other types ofvehicles (e.g., watercraft).

As used herein, the terms “trajectory” and “trajectories” may refer to apath (e.g., a path through a geospatial area, etc.) with positions ofthe AV along the path with respect to time, where a “path” generallyimplies a lack of temporal information, one or more paths for navigatingan AV in a roadway for controlling travel of the AV on the roadway. Atrajectory may be associated with a map of a geographic area includingthe roadway. In such an example, the path may traverse a roadway, anintersection, an other connection or link of the road with another road,a lane of the roadway, objects in proximity to and/or within the road,and/or the like. For example, a trajectory may define a path of travelon a roadway for an AV that follows each of the rules (e.g., the path oftravel does not cross a yellow line, etc.) associated with the roadway.In such an example, an AV that travels over or follows the trajectory(e.g., that travels on the roadway without deviating from thetrajectory, etc.) may obey each of the rules or account for constraints(e.g., objects in the roadway, does not cross the yellow line, etc.)associated with the roadway.

As used herein, “map data” includes data associated with a road (e.g.,an identity and/or a location of a roadway of a road, an identity and/orlocation of a segment of a road, etc.), data associated with an objectin proximity to a road (e.g., a building, a lamppost, a crosswalk, acurb of the road, etc.), data associated with a lane of a roadway (e.g.,the location and/or direction of a travel lane, a parking lane, aturning lane, a bicycle lane, etc.), data associated with trafficcontrol of a road (e.g., the location of and/or instructions associatedwith lane markings, traffic signs, traffic lights, etc.), and/or thelike. According to some embodiments, a map of a geographic locationincludes one or more routes (e.g., a nominal route, a driving route,etc.) that include one or more roadways. According to some non-limitingembodiments or aspects, map data associated with a map of the geographiclocation associates the one or more roadways with an indication ofwhether an AV can travel on that roadway.

As used herein, a “road” refers to a paved or an otherwise improved pathbetween two places that allows for travel by a vehicle (e.g., AV).Additionally or alternatively, a road includes a roadway and a sidewalkin proximity to (e.g., adjacent, near, next to, abutting, touching,etc.) the roadway. In some non-limiting embodiments or aspects, aroadway includes a portion of a road on which a vehicle is intended totravel and is not restricted by a physical barrier or by separation sothat the vehicle is able to travel laterally. Additionally oralternatively, a roadway (e.g., a road network, one or more roadwaysegments, etc.) includes one or more lanes in which a vehicle mayoperate, such as, a travel lane (e.g., a lane upon which a vehicletravels, a traffic lane, etc.), a parking lane (e.g., a lane in which avehicle parks), a turning lane (e.g., a lane in which a vehicle turnsfrom), and/or the like. Additionally or alternatively, a roadwayincludes one or more lanes in which a pedestrian, bicycle, or othervehicle may travel, such as, a crosswalk, a bicycle lane (e.g., a lanein which a bicycle travels), a mass transit lane (e.g., a lane in whicha bus may travel), and/or the like. According to some non-limitingembodiments, a roadway is connected to another roadway to form a roadnetwork, for example, a lane of a roadway is connected to another laneof the roadway and/or a lane of the roadway is connected to a lane ofanother roadway. In some non-limiting embodiments, an attribute of aroadway includes a road edge of a road (e.g., a location of a road edgeof a road, a distance of location from a road edge of a road, anindication whether a location is within a road edge of a road, etc.), anintersection, a connection, or a link of a road with another road, aroadway of a road, a distance of a roadway from another roadway (e.g., adistance of an end of a lane and/or a roadway segment or extent to anend of another lane and/or an end of another roadway segment or extent,etc.), a lane of a roadway of a road (e.g., a travel lane of a roadway,a parking lane of a roadway, a turning lane of a roadway, lane markings,a direction of travel in a lane of a roadway, etc.), one or more objects(e.g., a vehicle, vegetation, a pedestrian, a structure, a building, asign, a lamppost, signage, a traffic sign, a bicycle, a railway track, ahazardous object, etc.) in proximity to and/or within a road (e.g.,objects in proximity to the road edges of a road and/or within the roadedges of a road), a sidewalk of a road, and/or the like.

As used herein, navigating (e.g., traversing, driving, etc.) a route mayinvolve the creation of at least one trajectory or path through the roadnetwork and may include any number of maneuvers or an evaluation of anynumber of maneuvers (e.g., a simple maneuver, a complex maneuver, etc.),such as, a maneuver involving certain driving conditions, such as, densetraffic, where successfully completing a lane change may require acomplex maneuver, like speeding up, slowing down, stopping, or abruptlyturning, for example, to steer into a narrow gap between vehicles,pedestrians, or other objects (as detailed herein) in a destinationlane. Additionally, in-lane maneuvers may also involve an evaluation ofany number of maneuvers, such as, a maneuver to traverse a lane split,an intersection (e.g., a three-leg, a four-leg, a multileg, aroundabout, a T-junction, a Y-intersection, a traffic circle, a fork,turning lanes, a split intersection, a town center intersection, etc.),a travel lane (e.g., a lane upon which a vehicle travels, a trafficlane, etc.), a parking lane (e.g., a lane in which a vehicle parks), aturning lane (e.g., a lane from which a vehicle turns, etc.), merginglanes (e.g., two lanes merging to one lane, one lane ends and mergesinto a new lane to continue, etc.), and/or the like. Maneuvers may alsobe based on current traffic conditions that may involve an evaluation ofany number of maneuvers, such as, a maneuver based on a current trafficspeed of objects in the roadway, current accidents or other incidents inthe roadway, weather conditions in the geographic area (e.g., rain, fog,hail, sleet, ice, snow, etc.), or road construction projects. Inaddition, maneuvers may also involve an evaluation of any number ofobjects in and around the roadway, such as, a maneuver to avoid anobject in proximity to a road, such as, structures (e.g., a building, arest stop, a toll booth, a bridge, etc.), traffic control objects (e.g.,lane markings, traffic signs, traffic lights, lampposts, curbs of theroad, a gully, a pipeline, an aqueduct, a speedbump, a speed depression,etc.), a lane of a roadway (e.g., a parking lane, a turning lane, abicycle lane, etc.), a crosswalk, a bicycle lane (e.g., a lane in whicha bicycle travels), a mass transit lane (e.g., a travel lane in which abus, a train, a light rail, and/or the like may travel), objects inproximity to and/or within a road (e.g., a parked vehicle, a doubleparked vehicle, vegetation, a lamppost, signage, a traffic sign, abicycle, a railway track, a hazardous object, etc.), a sidewalk of aroad, and/or the like.

As used herein, “sensor data” includes data from one or more sensors.For example, sensor data may include light detection and ranging (LiDAR)point cloud maps (e.g., map point data, etc.) associated with ageographic location (e.g., a location in three-dimensional spacerelative to the LiDAR system of a mapping vehicle) of a number of points(e.g., a point cloud) that correspond to objects that have reflected aranging laser of one or more mapping vehicles at the geographiclocation. As an example, sensor data may include LiDAR point cloud datathat represents objects in the roadway, such as, other vehicles,pedestrians, cones, debris, etc.

A vehicle computing system can control the AV on a trajectory (e.g., apath) as it traverses a roadway or traverses between roadways (e.g., alateral transition from a first roadway to a second roadway, a lineartransition from a first roadway to a second roadway, a merger from afirst roadway into a second roadway, and/or the like). The appropriatetrajectory can be affected by objects that are encountered by the AValong the route, such as, other vehicles, pedestrians, signage, and thelike. In some non-limiting embodiments, a vehicle computing system mayemploy topological planning techniques to navigate these objects. Insuch topological planning techniques, the objects and the actionsavailable in response to such objects may be represented as ahierarchical architecture for generating trajectories for the AV. Insome non-limiting embodiments, this process of trajectory planning caninclude generating sets of candidate constraints that specify semanticlongitudinal and lateral actions (e.g., stop at stop line, pass objecton right); optimizing a trajectory for each candidate constraint setwhere the trajectory satisfies the constraints and is feasible withrespect to the dynamic limits of the AV; and selecting the bestcandidate trajectory for execution. An example of topological planningin AV driving is set forth in U.S. application Ser. No. 16/597,283, thedisclosure of which is expressly incorporated herein by reference.

Referring now to FIG. 1A, FIG. 1A provides an environment 100 in whichdevices, systems, and/or methods, herein, may be implemented.Environment 100 can include AV 102 and actor 104.

In some non-limiting embodiments or aspects, AV 102 evaluates risks andeffects on comfort caused by future proximity or margins to otherobjects. The risk or discomfort rating is referred to as a score (e.g.,a cost, etc.). Motion planning generates multiple candidate trajectoriesat a time using this score, among others, to select a trajectory tofollow.

In some examples, generating candidate trajectories involves a motionplanning task to generate constraint sets that are composed of possibleactions (e.g., constraints, etc.), for each object. Each of theseconstraints may define a sequence of actions to be taken over particulartime intervals. For instance, an action could involve AV 102 stayingahead of actor 104 from 4-6 seconds in the future. In such an example, aconstraint associated with this action may involve AV 102 staying behindactor 104 from 0-2 seconds in the future, passing it from 2-4 seconds inthe future, and staying ahead of it from 4-6 seconds in the future. Acandidate trajectory is then generated for each constraint set. In suchan example, constraints are used together in groups by an optimizationroutine to compute each trajectory.

In some examples, AV 102 may perform binary collision checking todetermine a time at which a collision is expected. Binary collisionchecking can be implemented by generating convex hulls for AV 102 andpredicted poses over a sampling of time intervals for actor 104.Geometric intersection checks may then be computed, findingintersections between AV 102 and actor 104 using the convex hulls ateach time interval. Convex hulls are able to identify collisions betweenfast moving actors and ensure they are not omitted without requiring avery dense temporal sampling. Even when implemented correctly andefficiently, however, binary collision checking includes pitfalls. Forexample, it may be inefficient or unable to appropriately evaluatepotential risks, without a dense sampling of predicted actortrajectories that would not be feasible for real time applications.Additionally, necessary consideration of comfort related to margins maynot be captured.

In some non-limiting embodiments or aspects, computing scores formargins associated with other actors, may also rely on constraints. Suchmethods are effectively evaluating whether constraints are satisfied.However, such methods may fall short in a number of important ways.First, there is no guarantee that constraints can capture all of thegenerated trajectory's relevant interactions, as constraints aredetermined a priori, which may cause some risks to be underrepresentedor unreported in the score. Second, heuristics that are used to modifyinteraction information are often applied to constraints to improve agenerated result, but such results may include less information orinclude inconsistent data for scoring. Third, two candidate trajectoriesthat are identical in terms of their spatiotemporal states may betreated differently based on constraints that were used to generatethem. The result could be a suboptimal trajectory selection for AV 102because of an incorrectly computed score for the margins to otheractors.

With continued reference to FIG. 1A, to illustrate problems that must beovercome, environment 100 is shown in FIG. 1A. AV 102 is traversing theroadway, coming from behind actor 104 (e.g., a parked vehicle, astationary object, etc.) and crossing pedestrian 106. In such anenvironment 100, three constraint sets may be found in this scenario.First candidate trajectory 108 is configured to stop AV 102 for actor104. Second trajectory 112 is configured to stop AV 102 when crossingpedestrian 106 is predicted to be present in the AV lane moving acrossthe lane on pedestrian trajectory 110, in a scenario where passing thepedestrian is too risky. Final trajectory 114 includes a constraint formoving ahead of the pedestrian, while the pedestrian is traversing theAV lane on pedestrian trajectory 110.

Based on final candidate trajectory 114, we see that because it hasveered around actor 104, final candidate trajectory 114 interacts withpedestrian trajectory 110 in a different location than was used forconfiguring the constraint, which was based off of a reference path (RP)of the AV. A margin scoring implementation that relies on theconstraints would, thus, misrepresent the true margins.

Provided are improved systems, methods, and computer program productsfor constraint independent scoring of margins for maneuvering an AVtraversing a roadway. In some non-limiting embodiments or aspects, asystem, such as, a vehicle computing system including a motion planner,may include at least one processor programmed or configured to receiveinformation associated with one or more forecasts of one or more actorsin a roadway, determine the relevance of each forecast with thetrajectory of the AV, regenerate a distance table for the relevanttrajectory, using an original distance table which was previouslygenerated for processing constraints, generate a plurality of marginsfor the AV to evaluate, categorize the margins into a plurality ofmargin types for providing information about risks and effects onpassenger comfort associated with future proximity of AV 102 to actor104, and classify an interaction between AV 102 and actor 104 based on aplurality of margins, and generate continuous scores for each candidatetrajectory that is also within the margin of the actor generated for therelevant trajectory.

In some non-limiting embodiments or aspects, controlling the AV involvesmotion planning to determine an optimal trajectory from a plurality oftrajectories, and evaluate potential risks of each candidate trajectory,to optimize a candidate trajectory selected to control the AV by amargin scoring subsystem of the vehicle computing system.

According to the systems, methods, and computer program productsdescribed herein, provided are comprehensive and continuous evaluationof risk and comfort aspects of autonomous navigation, in addition tocomprehensive and continuous evaluation of risk and comfort aspects ofinteractions with other actors in the roadway, may be provided, providedmore sufficiently, and provided more efficiently.

Objective evaluation of candidate trajectories based on associatedgeometry in relation to the predictions of other actors, reduces andeliminates computational inefficiencies and inconsistent results, andfurther provides runtime efficiency through careful reuse ofintermediate results, consistent frameworks for evaluating multipleclasses of margins, handles right of way (“ROW”) and uncertaintyconsiderations, and provides margin data that is useful for thecalculation of other scores.

Referring now to FIG. 1B, FIG. 1B provides environment 120 in whichdevices, systems, and/or methods, herein, may be implemented.Environment 120 includes AV 102 and actor 124. AV 102, in somenon-limiting embodiments or aspects, may be programmed or configured todetermine a forecast relevance. It is desirable to determine whether agiven forecast is potentially-relevant to AV 102, and may reduce oreliminate unnecessary computations while more efficiently operating forruntime. In some non-limiting embodiments or aspects, forecast relevanceis performed by reusing some of the properties computed duringconstraint generation.

With continuing reference to FIG. 1B, as shown, AV 102 is planning todrive past actor 124. During constraint generation, reference path 126may be used to determine minimum distance 130 to actor 124. For example,AV 102, in scoring candidate trajectory 128, determines maximumdeviation 132 between reference path 126 and candidate trajectory 128.Since a maximum lateral margin that is deemed relevant to scoring may bepredetermined (e.g., previously known, etc.), one or more processors canbe programmed or configured to determine whether distance 134 betweenactor 124 and candidate trajectory 128 is relevant, before conductingany expensive geometric spatial checks. If the margins are not relevant,then expensive and unnecessary computations based on a predictedforecast about actor 124 are not needed and can be eliminated.

Referring now to FIG. 2 , FIG. 2 is a diagram of an example vehiclecomputing system 200 in which devices, systems, and/or methods,described herein, may be implemented. As shown in FIG. 2 , vehiclecomputing system 200 includes communication network 220, sensor system222, vehicle controller 224, and vehicle control system 226. Vehiclecomputing system 200 may interconnect (e.g., establish a connection tocommunicate and/or the like) via communication network 220 to remotedata and processing systems (e.g., sources, computing devices, externalcomputing systems, etc.) of data store(s) 228 and central server(s) 230,for example, communication interface 232 of vehicle computing system 200may utilize wired connections and/or wireless connections to provide aninput or output exchange with local vehicle systems (e.g., one or moresystems of AV 102, etc.).

With continued reference to FIG. 2 , AV 102 may, additionally oralternatively, include sensor system 222, vehicle controller 224,vehicle control system 226, central map data store 234, and driverinterface system 236. AV 102 may further include certain components (notshown here) included in vehicles, such as, an engine, wheels, steeringwheel, transmission, etc., which may be controlled by vehicle controlsystem 226 or, alternatively, vehicle controller 224, using a variety ofcommunication signals and/or commands, such as, for example,acceleration signals or commands, deceleration signals or commands,steering signals or commands, braking signals or commands, etc.

In some non-limiting embodiments or aspects, vehicle computing system200 includes components for autonomous operation of AV 102 to store orretrieve (e.g., request, receive, etc.) vehicle information from one ormore data stores 228 and/or one or more central servers 230 viacommunication interface 232. For example, vehicle computing system 200may synchronize (e.g., update, change, etc.) data of data store(s) 228or interfaces of driver interface system 236, with map data (e.g., aportion of map data) in central map data store 234 or vehicle controlinformation processed in central servers 230 as AV 102 is traversing aroadway. Multiple AVs may be coupled to each other and/or coupled todata stores 228 and/or central servers 230 by communication interface232. Communication interface 232 may be any type of network, such as, alocal area network (LAN), a wide area network (WAN), such as, theInternet, a cellular network, a satellite network, or a combinationthereof, and may be wired or wireless. Remote data store(s) 228 may beany kind of data stores, such as, without limitation, map data stores,traffic information data stores, user information data stores, point ofinterest data store(s), or any other type of content data store(s).Central server(s) 230 may be any kind of servers or a cluster ofservers, such as, without limitation, Web or cloud servers, applicationservers, backend servers, or a combination thereof.

Sensor system 222 may include one or more sensors that are coupled tovehicle controller 224 and/or otherwise connected or included within AV102 of FIGS. 1A and 1B. Examples of such sensors include, withoutlimitation, a light detection and ranging (LiDAR) system, a radiodetection and ranging (RADAR) system, one or more cameras (e.g., visiblespectrum cameras, infrared cameras, etc.), temperature sensors, positionsensors, location sensors (e.g., global positioning system (GPS), etc.),fuel sensors, speed sensors, odometer sensors, motion sensors (e.g.,inertial measurement units (IMU), accelerometer, gyroscope, etc.),object detection sensors, such as, one or more camera humidity sensors,environmental sensors (e.g., a precipitation sensor and/or ambienttemperature sensor) occupancy sensors, or the like. The sensor data caninclude information that describes the location of objects within thesurrounding environment of AV 102, information about the environmentitself, information about the motion of AV 102, information about aroute of AV 102, or the like.

With continued reference to FIG. 2 , vehicle controller 224 may receivedata collected by sensor system 222 and analyze it to provide one ormore vehicle control instructions to vehicle control system 226. Vehiclecontroller 224 may include, without limitation, location system 238,perception system 242, prediction system 244, and motion planning system246.

Location system 238 may include and/or may retrieve map data (e.g., mapinformation, etc.) from central map data store 234 that providesdetailed information about the surrounding environment of AV 102. Insome non-limiting embodiments or aspects, location system 238 mayinclude and/or may retrieve map data (e.g., map information, etc.) fromcentral map data store(s) 234, central server(s) 230, central map datastore 234, and/or a combination, that provides detailed informationabout the surrounding environment of AV 102. The map data can provideinformation regarding: the identity and location of different roadways,road segments, buildings, or other items; the location and directions oftraffic lanes (e.g., the location and direction of a parking lane, aturning lane, a bicycle lane, or other lanes within a particularroadway); traffic control data (e.g., the location and instructions ofsignage, traffic lights, or other traffic control devices); and/or anyother map data that provides information that assists vehicle controller224 in analyzing the surrounding environment of AV 102. In somenon-limiting embodiments or aspects, the map data may also includereference path information that correspond to common patterns of vehicletravel along one or more lanes such that the motion of the object isconstrained to the reference path (e.g., locations within traffic laneson which an object commonly travels). Such reference paths may bepre-defined, such as, the centerline of the traffic lanes. Optionally,the reference path may be generated based on historical observations ofvehicles or other objects over a period of time (e.g., reference pathsfor straight line travel, lane merge, a turn, or the like).

In some non-limiting embodiments or aspects, location system 238 mayalso include and/or may receive information relating to a trip or routeof a user, real-time traffic information on the route, and/or the like.

Location system 238 may include and/or may be in communication withrouting module 240 that generates a navigation route from a startposition to a destination position for AV 102. Routing module 240 mayaccess central map data store 234 to identify possible routes and roadsegments that a vehicle can travel on to get from the start position tothe destination position. Routing module 240 may score the possibleroutes and identify a preferred route to reach the destination. Forexample, routing module 240 may generate a navigation route thatminimizes Euclidean distance traveled or other cost function during theroute and may further access the traffic information and/or estimatesthat can affect an amount of time it will take to travel on a particularroute. Depending on implementation, routing module 240 may generate oneor more routes using various routing methods, such as, Dijkstra'salgorithm, Bellman-Ford's algorithm, or other algorithms. Routing module240 may also use the traffic information to generate a navigation routethat reflects expected conditions of the route (e.g., current day of theweek or current time of day, etc.), such that a route generated fortravel during rush-hour may differ from a route generated for travellate at night. Routing module 240 may also generate more than onenavigation route to a destination and send more than one of thesenavigation routes to a user for selection by the user from among variouspossible routes.

Based on the sensor data provided by sensor system 222 and informationobtained by location system 238, perception system 242 may determineperception information of the surrounding environment of AV 102 duringtravel from the start position to the destination along the preferredroute. The perception information may represent what an ordinary driverwould perceive in the surrounding environment of a vehicle. Theperception data may include information relating to one or more objectsin the environment of AV 102. For example, perception system 242 mayprocess sensor data (e.g., LiDAR or RADAR data, camera images, etc.) inorder to identify objects and/or features in the geospatial area of AV102. The objects may include traffic signals, roadway boundaries, othervehicles, pedestrians, and/or obstacles, etc. Perception system 242 mayuse any now or hereafter known object recognition algorithms, videotracking algorithms, and computer vision algorithms (e.g., track objectsframe-to-frame iteratively over a number of time periods) to determinethe perception.

In some non-limiting embodiments or aspects, perception system 242 mayalso determine, for one or more identified objects in the environment,the current state of the object. The state information may include,without limitation, for each object: current location; current speedand/or acceleration; current heading; current orientation;size/footprint; type (e.g., vehicle vs. pedestrian vs. bicycle vs.static object or obstacle); and/or other state information.

Prediction system 244 may predict the future locations, trajectories,and/or actions of the objects based at least in part on perceptioninformation (e.g., the state data for each object) received fromperception system 242, the location information received from locationsystem 238, the sensor data, and/or any other data that describes thepast and/or current state of the objects, AV 102, the surroundingenvironment, and/or their relationship(s). For example, if an object isa vehicle and the current driving environment includes an intersection,prediction system 244 may predict whether the object will likely movestraight forward or make a turn. If the perception data indicates thatthe intersection has no traffic light, prediction system 244 may alsopredict whether the vehicle may fully stop prior to entering theintersection. Such predictions may be made for a given time horizon(e.g., 5 seconds in the future). In certain embodiments, predictionsystem 244 may provide the predicted trajectory or trajectories for eachobject to motion planning system 246.

Motion planning system 246 may determine a motion plan for AV 102 basedon the perception data and/or the prediction data. Specifically, givenpredictions about the future locations of proximate objects and otherperception data, motion planning system 246 can determine a motion planfor AV 102 that best navigates AV 102 relative to the objects at theirfuture locations.

In some non-limiting embodiments or aspects, motion planning system 246may receive the predictions from prediction system 244 and make adecision regarding how to handle objects in the environment of AV 102.For example, for a particular object (e.g., a vehicle with a givenspeed, a direction, a turning angle, etc.), motion planning system 246decides whether to overtake, yield, stop, and/or pass based on, forexample, traffic conditions, map data, state of AV 102, etc. In somenon-limiting embodiments or aspects, for a given object, motion planningsystem 246 may decide a course to handle the object and may determineone or more safe actions for responding to the presence of the object.For example, for a given object, motion planning system 246 may decideto pass the object and then may determine whether to pass on the leftside or right side of the object (including motion parameters, such as,speed and lane change decisions). Motion planning system 246 may alsoassess the risk of a collision between a detected object and AV 102. Ifthe risk exceeds an acceptable threshold, AV 102 determines whether thecollision can be avoided if the AV follows a defined vehicle trajectory,and, in such an example, can implement one or more dynamically generatedemergency maneuvers in a pre-defined time period (e.g., N milliseconds)in reaction to the potential collision. If the collision can be avoided,then vehicle controller 224 may transmit appropriate controlinstructions to vehicle control system 226 for execution to perform acautious maneuver (e.g., mildly slow down, accelerate, change lane, orswerve). In contrast, if the collision cannot be avoided, then vehiclecontroller 224 may transmit appropriate control instructions to vehiclecontrol system 226 for the execution of an emergency maneuver (e.g.,brake and/or change direction of travel).

Furthermore, motion planning system 246 also plans a trajectory(“trajectory generation”) for AV 102 to travel on a given route (e.g., anominal route generated by routing module 240). The trajectory specifiesthe geospatial path for AV 102, as well as, a velocity profile. Thecontroller converts the trajectory into control instructions for vehiclecontrol system 226 including, but not limited to, throttle/brake andsteering wheel angle commands. Trajectory generation may involve makingdecisions relating to lane changes, such as, without limitation, whethera lane change is required, where to perform a lane change, and when toperform a lane change. Specifically, one objective of motion planningsystem 246 is to generate a trajectory for the motion of the vehiclefrom a start position to a destination on the nominal route, taking intoaccount the perception and prediction data.

Motion planning system 246 may generate the trajectory by performingtopological planning using the topological planning techniques describedherein to generate a set of constraints for each of a plurality oftopologically distinct classes of trajectories, optimizing a singlecandidate trajectory for each class, and scoring the candidatetrajectories to select an optimal trajectory. Topological classes aredistinguished by the discrete actions taken with respect to obstacles orrestricted map areas. Specifically, all possible trajectories in atopologically distinct class perform the same action with respect toobstacles or restricted map areas. Obstacles may include, for example,static objects, such as, traffic cones and bollards, or other roadusers, such as, pedestrians, cyclists, and cars (e.g., moving cars,parked cars, double parked cars, etc.). Restricted map areas mayinclude, for example, crosswalks and intersections. Discrete actions mayinclude, for example, to stop before or proceed through, to track aheador behind, or to pass on the left or right of an object (e.g., obstacle,constraint, etc.).

Motion planning system 246 may use the preferred route informationprovided by routing module 240 in combination with perception data andprediction data to select the optimal trajectory, as discussed below.

As discussed above, planning and control data regarding the movement ofAV 102 is generated by motion planning system 246 of vehicle controller224 that is transmitted to vehicle control system 226 for execution.Vehicle control system 226 may, for example, control braking via a brakecontroller; direction via a steering controller; speed and accelerationvia a throttle controller (in a gas-powered vehicle), or a motor speedcontroller (such as, a current level controller in an electric vehicle);a differential gear controller (in vehicles with transmissions); and/orother controllers.

In the various embodiments discussed in this document, the descriptionmay state that the vehicle or a controller included in the vehicle(e.g., in an on-board computing system) may implement programminginstructions that cause the controller to make decisions and use thedecisions to control operations of one or more vehicle systems via thevehicle control system of the vehicle. However, the embodiments are notlimited to this arrangement, as in various embodiments the analysis,decision making, and/or operational control may be handled in full or inpart by other computing devices that are in electronic communicationwith the vehicle's on-board controller and/or vehicle control system.Examples of such other computing devices include an electronic device(such as, a smartphone) associated with a person who is riding in thevehicle, as well as, a remote server that is in electronic communicationwith the vehicle via a wireless network. The processor of any suchdevice may perform the operations that will be discussed below.

With further reference to FIG. 2 , communication interface 232 may beconfigured to allow communication between AV 102 and external systems,such as, for example, external devices, sensors, other vehicles,servers, data stores, databases etc. Communication interface 232 mayutilize any now or hereafter known protocols, protection schemes,encodings, formats, packaging, etc., such as, without limitation, Wi-Fi,an infrared link, Bluetooth, etc. Driver interface system 236 may bepart of peripheral devices implemented within AV 102 including, forexample, a keypad, a touch screen display device (such as, a graphicaluser interface GUI)), a microphone, and a speaker, etc. For example, AV102 may include a GUI on which is displayed a trajectory of AV 102, suchas, by indicating an upcoming route of AV 102 as it navigates around oneor more objects. The trajectory displayed on GUI may be one that isdetermined through the topological planning process described herein.

Referring now to FIG. 3 , FIG. 3 is a step diagram of process 300 ofvehicle computing system 200 for generating constraint independent andcontinuous margin scores that can be used by AV 102 to score candidatetrajectories for maneuvering AV 102 while traversing a roadway. In somenon-limiting embodiments or aspects, one or more steps of process 300for generating and evaluating continuous scores for margins to actors inthe roadway may be performed (e.g., completely, partially, and/or thelike) by AV 102 (e.g., one or more devices of AV 102). In somenon-limiting embodiments or aspects, one or more of the steps of process300 may be performed (e.g., completely, partially, and/or the like) byvehicle computing system 200 (e.g., one or more devices of vehiclecomputing system 200, one or more processors of vehicle computing system200), or by motion planning system 246 (e.g., one or more devices ofmotion planning system 246.

In some non-limiting embodiments or aspects, at first step 302, one ormore forecasts for one or more actors are received, obtained, orgenerated. For example, vehicle computing system 200 receives forecastinformation associated with one or more predicted trajectories of one ormore actors in a roadway. At step 304, vehicle computing system 200iterates over each actor, to determine predictions about actor 104 inthe roadway of AV 102. At step 306, vehicle computing system 200iterates each forecast to determine forecasts of each actor separately.

In some non-limiting embodiments or aspects, at step 308, it isdetermined whether a forecast (e.g., a predicted trajectory of actor104) is relevant to AV 102. For example, vehicle computing system 200,determines a relevant trajectory of an actor based on correlating aforecast for one or more predicted trajectories of the actor with thetrajectory of the AV. Determining a relevant predicted trajectory of anactor according to some non-limiting embodiments or aspects is discussedin more detail hereinabove with respect to FIG. 1B.

In some non-limiting embodiments or aspects, at step 310 the distancetable is regenerated for a given candidate trajectory. For example,vehicle computing system 200 regenerates a distance table for therelevant trajectory, using an original distance table which waspreviously generated for processing constraints. In such an example,regenerating the distance table regenerates (e.g., generates, etc.) adistance table for each actor given a candidate trajectory. In someexamples, the distance table has been previously generated using areference path. An example of topological planning in autonomous vehicledriving is set forth in U.S. application Ser. No. 17/172,530, filed onFeb. 10, 2021, the disclosure of which is expressly incorporated hereinby reference. A distance table may include an interface to query lateraldistances to, or alternatively, from actor 103, by interval locationalong a reference path at a time interval. In order to reason about acandidate trajectory or object in the proximity of AV 102, a distancetable may be used to generate an expected margin to one or more actors.In some non-limiting embodiments or aspects, the distance table must beregenerated for each potentially-relevant actor against a candidatetrajectory that is being scored. In some non-limiting embodiments oraspects, regenerating the distance table changes the distance table. Forexample, the distance table may be regenerated to change a previousdistance table provided for processing constraint sets using a referencepath of AV 102. In some examples, the reason for regenerating thedistance table is to account for deviations between a candidatetrajectory and a reference path used to generate or develop one or morecandidate trajectories. The candidate trajectories may deviatesignificantly from the reference path, both laterally, as seen in FIG.1A, and longitudinally, which affects the longitudinal margins to movingactors, such as, the pedestrian in FIG. 1B. Therefore, it may benecessary to regenerate a table to account for a candidate trajectory,instead of a reference path or some other generic driving path.

At step 312, longitudinal margins are determined. The longitudinalmargin costs are computed as both distance and time margins. Thetemporal longitudinal margin for a given time interval considers howlong AV 102 has until actor 104 reaches it. In some examples, thisfeature is relevant to actors traveling in areas where bothcross-traffic and in-lane actions are necessary. Temporal longitudinalmargins alone are insufficient to reason about actor margins and mostevident in scenarios where actors are moving at low speeds and, thus,the temporal margins may be large despite AV 102 having a small spatialmargin to actor 104.

In some non-limiting embodiments or aspects, vehicle computing system200 generates a temporal longitudinal margin associated with a relevanttime interval based on time remaining until AV 102 reaches actor 104. Insome examples, vehicle computing system 200 generates a temporallongitudinal margin for a relevant time interval, by determining aposition of the AV on a reference path and determining a proximate timeinterval (e.g., closest, within a threshold distance, etc.) where alocation of a longitudinal interval of actor 104 overlaps with that ofthe position.

In some non-limiting embodiments or aspects, vehicle computing system200 generates a spatial longitudinal margin that provides informationfor determining a proximity of an actor when moving at low speeds wherethe temporal longitudinal margins may be large despite AV 102 having asmall spatial margin to the actor. In such an example, generating thespatial longitudinal margin includes, for each relevant time interval,determining a location interval (e.g., reference path location interval,longitudinal interval, etc.) of actor 104 in a candidate trajectory ofAV 102, where actor 104 is deemed to be within a threshold proximity(e.g., within a threshold, sufficiently close, within a predetermineddistance, etc.) of the candidate trajectory, such that, for example,actor 104 is considered to be in the path of AV 102. Vehicle computingsystem 200, then determines the longitudinal spatial margin based on adelta of a location interval of AV 102 and the actor's locationinterval. In some non-limiting embodiments or aspects, a delta comprisesthe differences between the two positions, such as, for example, a timeinterval. In other examples, in addition to time interval, the deltaincludes differences between object positions at different times, adifference in speed, variations in travel patterns, a separationdistance, and/or the like. The longitudinal spatial margin is determinedbased on the delta.

At step 314, lateral margins are determined. For example, vehiclecomputing system 200 determines the lateral margin distance as theminimum lateral offset value over the longitudinal location interval ofAV 102 for the time interval being evaluated. Determining lateralmargins, according to some non-limiting embodiments or aspects, isdiscussed in more detail herein with respect to FIG. 6 .

In some non-limiting embodiments or aspects, for more efficient results,vehicle computing system 200 generates temporal and spatial longitudinalmargins for a sampling of various lateral margin thresholds. The marginsfor each threshold may have a configurable weight that may be applied ina final scoring stage. Determining longitudinal margins, according tosome non-limiting embodiments or aspects, is discussed in more detailherein with respect to FIGS. 4 and 5 .

At step 316, vehicle computing system 200 performs interactionclassification between AV 102 and actor 104. Interaction classificationis important for generating margin scores to remove uncertainty, todetermine right of way, and to account for a predicted collisionseverity. Interaction classification determines these properties. Thesteps for interaction classification according to some non-limitingembodiments or aspects is discussed in more detail herein with respectto FIG. 8 .

At step 318, for each relevant forecast (e.g., a predicted trajectory),the longitudinal margins are scored at step 320, and the lateral marginsare scored at step 322. Vehicle computing system 200 determines scoresfor margins that can be used to makes sure that the AV does not collidewith objects and also are used to maintain a safe speed and safedistance.

Particular technological improvements over other systems are included,such as, trajectory optimization where it would be necessary to comparediscrete samples. For example, sampling of trajectories at a high rate,such as, every second, is known to be inefficient and computationallyexpensive when used exclusively. In addition, using only discretelyselected samples may overlook, miss, or ignore worst case interactions,such as, where an AV or object is moving across an intersection at avery high rate of speed. In such an example, the AV may determine thatis safe to cross ahead of the object, but unaccounted for conflicts mayarise after starting, such as, for example, when the object may passmuch closer than predicted to an actor, causing undesirable results.

In some non-limiting embodiments or aspects, the margins are scored. Thescoring margins can be used to determine fault, such as, determining whohad the right of way (e.g., an actor, AV, etc.). In some examples,scoring margins can be used to determine whether AV 102 was ahead ofactor 104. In another example, vehicle computing system 200 maydetermine the AV-side of action (e.g., ahead of AV, behind AV, unknown,or undetermined) based on the scoring margins. The scoring marginseliminate unnecessary computations, such as, for example, when nodistance interval for a car exists at an interval. In this way, vehiclecomputing system 200 can eliminate expensive computations determiningpositioning of cars with respect to each other.

In some non-limiting embodiments or aspects, vehicle computing system200 determines cost of a candidate trajectory based on scoring margins.For example, vehicle computing system 200 may use scoring margins todetermine right of way. In such an example, scoring margins can beprogrammed or configured for determining when or where AV 102 is aheadof actor 104. In this example, AV 102 may have more right of way andless risk (e.g., the actor following needs to make more decisions,etc.). Vehicle computing system 200 may also provide increases inpenalty, such as, when AV 102 is behind actor 104 (e.g., options aremainly falling on AV 102, etc.).

In some non-limiting embodiments or aspects, vehicle computing system200 may use scoring margins and geospatial information to detect acollision. For example, vehicle computing system 200 generatesinformation from scoring margins to detect potential collisions, suchas, when actor 104 moves according to forecast and AV 102 executes atrajectory. Vehicle computing system 200 may determine severity, fault,basic reasoning, and/or the like, about a trajectory of AV 102.

In some non-limiting embodiments or aspects, vehicle computing system200 generates continuous scores (e.g., continually determines orgenerates margin scores). The margin scores may include a penaltyconfigured by transforming a margin value at each time interval into apenalty. In some examples, margin scores may include a discount factorto account for increasing uncertainty about interactions that may beperformed in the future. The discount factor at each subsequent timeinterval may include an offset from a start of a planning cycle fordiscounting attenuated uncertainty. In some non-limiting embodiments oraspects, margin scores may include an actor-type factor. For example,vehicle computing system 200 may generate discounting parameters peractor type, including at least one of a vehicle type, acrosswalk-following pedestrian type, other pedestrian type, cyclisttype, other object type, and/or the like. The discount parameter mayapply based on interaction classifications. In some examples, vehiclecomputing system 200 may generate a social acceptability factor thatclassifies a decision taken by a candidate trajectory with respect tothe margins to a plurality of actors simultaneously. In the aboveexamples, vehicle computing system 200 controls AV 102 based oncontinuous scores, such as, for example, to maneuver with respect to aplurality of constraints in the roadway.

Margin scoring, according to some non-limiting embodiments or aspects,is further discussed in more detail herein with respect to FIGS. 7 and 8.

Referring now to FIG. 4 , FIG. 4 shows an exemplary temporallongitudinal margin generated for scoring a trajectory, such that, afeature of the temporal longitudinal margin is utility and relevance forscoring candidate trajectories for both cross-traffic objects andin-lane objects.

In some non-limiting embodiments or aspects, vehicle computing system200 generates a temporal longitudinal margin for predicting how long AV402 has before actor 404 reaches its location. For example, while AV 402is traversing candidate trajectory 408 during time interval [t1, t2],vehicle computing system 200 determines how long before a reference path126 of actor 404 (e.g., an object or vehicle in the roadway) intersectsany of candidate trajectory 408 of AV 402.

In some non-limiting embodiments or aspects, in order to determine apoint of intersection (e.g., overlap), vehicle computing system 200generates a distance margin 412 based on a predicted distance of actor404 from the longitudinal interval 406 (e.g., location interval 414) ofAV 402 at each time interval until an overlap is determined. Forexample, vehicle computing system 200 may check different distancemargins for an interceding time interval (e.g., [t₃, t₄]) and determineno overlap is present during the timer interval. In such an example,vehicle computing system 200 continues to generate distance margins,until it generates distance margin 412 at time interval [t₅, t₆]) basedon predicted actor poses 410 for actor 404 (e.g., object, etc.). In somenon-limiting embodiments or aspects, vehicle computing system 200determines at the time interval [t₅, t₆] that the location interval onthe reference path of actor 404 overlaps (e.g., intersects) the locationinterval 414 on candidate trajectory 408 of AV 402. The temporallongitudinal margin is then generated by determining a delta between thetwo time intervals, for example, a delta between time interval [t₅, t₆]and time interval [t₃, t₄].

In some non-limiting embodiments or aspects, predicted actor poses 410are obtained or retrieved for generating distance margin 412. In such anexample, vehicle computing system 200 retrieves or obtains lateraldistances that represent a distance to (or from) actor 404 fromcandidate trajectory 408, the lateral distances stored and searchable inthe distance table in correspondence with a time interval.

In some non-limiting embodiments or aspects, distance table provides aninterface to retrieve lateral distances to the actor (e.g., actors,objects, road end, lane end, etc.), the lateral distances stored andretrievable based on the time interval. The distance table may be sortedby time, so that vehicle computing system 200 may retrieve distances fora time interval of AV 402. The retrieved distances identify or determinethe population of predicted actor poses 410 for the time interval. Insuch an example, predicted actor poses 410 are generated for one actor.In some examples, predicted actor poses 410 may be generated formultiple actors for corresponding time intervals.

In some non-limiting embodiments or aspects, longitudinal margins aredefined with a particular lateral threshold. With reference to FIG. 4and FIG. 5 , the lateral threshold is set to 0m. However, otherthresholds can be used, such as, 1m, 2m, 3m, etc. In some examples, foroptimizing results, temporal longitudinal margins and spatiallongitudinal margins are computed for a sampling of various lateralmargin thresholds. In some non-limiting embodiments or aspects, marginsfor each threshold have a configurable weight that is applied in thefinal scoring stage. In some examples, these weights may be dependent onvarious factors that contribute to a measure of collision severity,including but not limited to the relative velocities, angles of impact,relative masses, and amounts of intersection between the AV and actorforecasts.

Referring now to FIG. 5 , FIG. 5 shows an exemplary spatial longitudinalmargin generated for scoring a trajectory. In some non-limitingembodiments or aspects, vehicle computing system 200 generates a spatiallongitudinal margin 516 based on predicting an interaction between AV502 and actor 504 in the roadway.

In some non-limiting embodiments or aspects, vehicle computing system200 generates a distance margin 512 for determining an associatedlongitudinal interval (e.g., reference path location interval, etc.) ofactor 504 during a time interval. For example, vehicle computing system200 determines predicted actor poses 510 for actor 504 (e.g., objectposes, etc.) during a time interval that may be used for generatingdistance margin 512 (e.g., a time interval as AV 502 traverses aroadway, etc.). In such an example, vehicle computing system 200 mayretrieve or obtain lateral distances from distance table correspondingto longitudinal interval 514 (e.g., reference path location interval,etc.) of AV 502, or other lateral distance when checking each timeinterval individually. In such an example, vehicle computing system 200generates and checks each time interval, and computes a longitudinalinterval (e.g., reference path location interval, etc.) over which theactor is deemed sufficiently close to the candidate trajectory 508, tobe considered in the path of AV 502. With reference to FIG. 5 , vehiclecomputing system 200 computes longitudinal interval 514 (e.g., thereference path location interval as shown) over which the actor isdeemed sufficiently close to the candidate trajectory 508, to beconsidered in the path of AV 502.

In some non-limiting embodiments or aspects, the distance table providesan interface to retrieve lateral distances to the actor (e.g., actors,objects, road end, lane end, etc.), the lateral distances are stored andretrievable based on the time interval, and returning a location thatmay intersect candidate trajectory 508 of AV 502 (e.g., the distancetable is regenerated to transition the distances from a reference pathbased location to a candidate trajectory based location). The distancetable may be sorted by time, so that vehicle computing system 200 mayretrieve distances for a time interval of AV 502. The retrieveddistances may identify or determine the population of predicted actorposes 510 within predicted distances around a candidate trajectory of AV502. In such an example, predicted actor poses 510 must be generated forone actor. In some examples, distance poses may be generated formultiple actors in a corresponding time interval.

In some non-limiting embodiments or aspects, vehicle computing system200 generates distance margin 512 based on predicted actor poses 510.For example, distance margin 512 demarcates a zone around actor 504, thezone being an area where interaction with AV 502 is predicted given thecandidate trajectory of AV 502 during the given time interval, such as,the interval for [t1, t2] as shown in FIG. 5 . In this example, vehiclecomputing system 200 determines, for time interval [t1, t2], alongitudinal interval 514 for actor 504 that is deemed sufficientlyclose (e.g., within a threshold, etc.) to the candidate trajectory toprovide a lateral tolerance over the time interval.

In some non-limiting embodiments or aspects, vehicle computing system200 determines the delta between longitudinal interval 506 of AV 502 atthe time interval on candidate trajectory 508 and longitudinal interval514 for actor 504. Longitudinal margin 516 is generated based on thedelta and is determined to provide the minimum spatial offset value forlongitudinal interval 506 at the current location of AV 502.

In some non-limiting embodiments or aspects, temporal margins of FIG. 4are insufficient by themselves to provide reasoning about actor margins.For example, in scenarios where actors are moving at low speeds,temporal margins may be large despite AV 502 having a small spatialmargin to the actor, creating a safety risk when a spatial longitudinalvalue is unavailable or incorrectly determined.

Referring now to FIG. 6 , FIG. 6 shows an exemplary lateral margingenerated for scoring a trajectory. In some non-limiting embodiments oraspects, vehicle computing system 200 generates a lateral margin 614based on predicting an interaction between AV 602 and actor 604 in theroadway.

In some non-limiting embodiments or aspects, vehicle computing system200 generates distance margin 612 based on a predicted position of theactor. For example, vehicle computing system 200 may determine predictedactor poses 610 for actor 604 (e.g., object, etc.) in the time intervalthat can be used for generating distance margin 612. In such an example,vehicle computing system 200 may retrieve or obtain lateral distances tothe actor, stored in the distance table and corresponding tolongitudinal interval 606 (e.g., reference path location interval, etc.)of AV 602.

In some non-limiting embodiments or aspects, the distance table providesan interface to retrieve lateral distances to the actor (e.g., actors,objects, road end, lane end, etc.), the lateral distances stored andretrievable based on the time interval and returning a location that mayintersect candidate trajectory 608 of AV 602 (e.g., the distance tableis regenerated to transition the distances from a reference path basedlocation to a candidate trajectory based location). The distance tablemay be sorted by time, so that vehicle computing system 200 may retrievedistances for a time interval of AV 602. The retrieved distances mayidentify or determine the population of predicted actor poses 610 withinpredicted distances around a candidate trajectory of AV 602. In such anexample, predicted actor poses 610 must be generated for one actor. Insome examples, distance poses may be generated for multiple actors in acorresponding time interval.

In some non-limiting embodiments or aspects, vehicle computing system200 generates distance margin 612 based on predicted actor poses 610.For example, distance margin 612 demarcates a zone around actor 604, thezone being an area where interaction with AV 602 is predicted given thecandidate trajectory of AV 602 during the given time interval, such as,the interval for [t₁, t₂] as shown in FIG. 6 . In this example, vehiclecomputing system 200 determines, for time interval [t₁, t₂], a lateralmargin 614 to actor 604, which is determined to be the minimum lateraloffset value over longitudinal interval 606 at the current location ofAV 602.

Referring now to FIG. 7 , FIG. 7 is a step diagram of process 700 thatoccurs at vehicle computing system 200 to determine and store an AV-sideof an actor and account for post-collision margins for AV 102. In somenon-limiting embodiments or aspects, one or more of the steps of process700 for generating and evaluating continuous scores for margins toactors in the roadway may be performed (e.g., completely, partially,and/or the like) by AV 102 (e.g., one or more devices of AV 102). Insome non-limiting embodiments or aspects, one or more of the steps ofprocess 700 may be performed (e.g., completely, partially, and/or thelike) by vehicle computing system 200 (e.g., one or more devices ofvehicle computing system 200, one or more processors of vehiclecomputing system 200), or by motion planning system 246 (e.g., one ormore devices of motion planning system 246.)

In some non-limiting embodiments or aspects, vehicle computing system200 uses margin scoring for determining the AV-side of the actor andhandling post-collision margins. In some examples, it is desirable touse different parameters for scoring longitudinal margins when the AV isstaying behind actor 104, in contrast to moving ahead of actor 104. Forthis reason, an AV-side of the actor is determined at each timeinterval. Additionally or alternatively, in the event that AV 102 isexpected to collide/intersect with a predicted trajectory of actor 104,additional care must be taken with the margins and AV-side of the actor.Once a pose of AV 102 and a predicted trajectory of actor 104 havecollided, the semantics of the margins are invalid. A naiveimplementation which does not handle this explicitly, may assign a lowercost to a trajectory which moves through an object faster, defininglarger margins on the other side for the remainder of the planninghorizon.

The technical solution described herein overcomes technical problems bysaturating margins and forward propagating the AV-side of the actorafter the first point of intersection, within each contiguous sequenceof time intervals where the action is occurring. Finally, if the AV sideof the actor is ambiguous initially, which would happen if the AV andactor intersect at their first point of interaction, then the AV side ofthe actor is resolved through a backward propagation process.

Process 700 initializes at a first step 702, when vehicle computingsystem 200 initializes a previous AV-side of actor to “unknown” and AVstatus is set to “no collision”. At step 704, vehicle computing system200 performs an iteration over each time interval. For example, vehiclecomputing system 200 iterates over time intervals to propagateinformation to each interval along the trajectory. For example, vehiclecomputing system 200 generates an AV-side of the actor at each timeinterval based on a distance margin, in addition to one or moreparameters for scoring longitudinal margins that can change based on theAV-side of the actor.

In some non-limiting embodiments or aspects, at step 706, vehiclecomputing system 200 determines whether a gap occurred, a gap includessituations where actor 104 was outside the reference path of AV 102 forone or more time intervals.

At step 708, if there was a gap, the previous AV-side of the actor isset to be unknown. At step 710, vehicle computing system 200 determinesif the AV-side of actor is altered (or unaltered) using a longitudinaldistance margin. Longitudinal margins are computed as both distanceintervals and time intervals. Temporal longitudinal margins for given atime interval determine a time (e.g., how long, etc.) when actor 104reaches AV 102. Spatial longitudinal margins determine a reference pathdistance interval over which actor 104 is deemed sufficiently close tothe candidate trajectory to be considered in the path of AV 102.

At step 712, if an unaltered AV-side of actor 104 is determined at step710, detect a collision when a longitudinal distance margin isdetermined to be zero. For example, vehicle computing system 200 detectsa collision if the longitudinal distance is determined to be zero. Atstep 714, vehicle computing system 200 detects a collision has occurredif no gap is determined at step 706, and the current AV-side of actor isaltered, and AV-side of the actor is set equal to previous AV-side ofthe actor.

In some non-limiting embodiments or aspects, at step 716, vehiclecomputing system 200 back propagates by iterating backwards for eachtime interval, and at step 718, setting the AV-side of the actor basedon the previous time interval, if the AV-side of the actor is unknownfor the current time interval.

Referring now to FIG. 8 , FIG. 8 is a step diagram for the interactionclassification process 800 that occurs at vehicle computing system 200to determine, classify, or store an interaction between AV 102 and actor104. In some non-limiting embodiments or aspects, one or more of thesteps of process 800 for generating and evaluating continuous scores formargins to actors in the roadway may be performed (e.g., completely,partially, and/or the like) by AV 102 (e.g., one or more devices of AV102). In some non-limiting embodiments or aspects, one or more of thesteps of process 800 may be performed (e.g., completely, partially,and/or the like) by vehicle computing system 200 (e.g., one or moredevices of vehicle computing system 200, one or more processors ofvehicle computing system 200), or by motion planning system 246 (e.g.,one or more devices of motion planning system 246.

In some non-limiting embodiments or aspects, at step 802, the processstarts with steps to identity an interaction classification timeinterval. At step 804, if a collision is expected at step 806, vehiclecomputing system 200 determines a first longitudinal margin indicating acollision by iterating longitudinal margins for one or more timeintervals until a collision is detected. At step 808, vehicle computingsystem 200 determines an instance of collision by iterating precise timesteps with polygon intersection checks.

At step 810, vehicle computing system 200 determines a time intervalwith the smallest spatial longitudinal margin, or if no smallest spatiallongitudinal margin, the smallest lateral margin. At step 812, toperform interaction classification for the identified time interval, atstep 814, vehicle computing system 200 determines if a candidatetrajectory is stationary. At step 816, vehicle computing system 200determines if increasing AV braking will reduce severity of predictedinteraction, at step 818, determine if actor is at fault for theinteraction.

Although the above systems, methods, and computer program products havebeen described in detail for the purpose of illustration based on whatis currently considered to be the most practical and preferredembodiments, it is to be understood that such detail is solely for thatpurpose and that the present disclosure is not limited to the describedembodiments but, on the contrary, is intended to cover modifications andequivalent arrangements that are within the spirit and scope of theappended claims. For example, it is to be understood that the presentdisclosure contemplates that, to the extent possible, one or morefeatures of any embodiment can be combined with one or more features ofany other embodiment.

What is claimed is:
 1. A computer-implemented method of maneuvering anautonomous vehicle (AV) traversing a roadway, comprising: receiving, byone or more processors of a vehicle computing system, forecastinformation associated with one or more predicted trajectories of one ormore actors in the roadway; regenerating, by the one or more processors,a distance table for a relevant trajectory of an action, using anoriginal distance table which was previously generated for processingconstraints, the relevant trajectory being based on a forecast for oneor more predicted trajectories of the actor with the trajectory of theAV; generating, by the one or more processors, a plurality of marginsfor the AV to evaluate, the plurality of margins being based on aplurality of margin types for providing information about risks andeffects on passenger comfort associated with a future proximity of theAV to the actor; generating, by the one or more processors, continuousscores for each candidate trajectory that is also within the margin ofthe actor generated for the relevant trajectory; and issuing, by the oneor more processors, a command to control the AV based on the continuousscores, to maneuver with respect to a plurality of constraints in theroadway.
 2. The computer-implemented method of claim 1, wherein theplurality of margins include at least one of a longitudinal margin or alateral margin and generating the plurality of margins furthercomprises: generating a temporal longitudinal margin associated with arelevant time interval based on time remaining until the AV reaches theactor, generating the temporal longitudinal margin, including for therelevant time interval: determining a position of the AV on a referencepath and the nearest time interval where a location of a longitudinalinterval of the actor overlaps with that of the position; and generatinga spatial longitudinal margin providing information for determiningproximity of the actor when moving at low speeds where the temporallongitudinal margins may be large despite the AV having a small spatialmargin to the actor, generating the spatial longitudinal margin includesfor each relevant time interval: determining lateral distance tableentries of the actor and determining a location interval of thereference path over which the actor is deemed within a threshold of thecandidate trajectory and considered in the reference path of the AV; andgenerating a lateral margin distance as the minimum lateral offset valueover a longitudinal location interval of the AV for the time intervalbeing evaluated.
 3. The computer-implemented method of claim 1, furthercomprising classifying an interaction between the AV and the actor,wherein the classifying the interaction comprises: in response topredicting a collision, performing an interaction classification toidentify a time step associated with an instance of the collision,wherein the interaction classification identifies the time step in atime sequence of a collision time interval that a first polygonintersection is found, wherein the first polygon intersection identifiesthe collision time interval based on a first longitudinal margin of aplurality of longitudinal margins that indicates a collision; andotherwise, for each time interval, performing an interactionclassification to identify a spatial longitudinal margin accounted to bethe minimum spatial longitudinal margin in the location interval of thereference path when the actor is predicted to be at a minimum distancefrom the candidate trajectory of the AV's path, or alternatively,identifying the time interval over the longitudinal location interval ofthe actor when it is predicted to have a minimum lateral offset valuefrom the candidate trajectory of the AV.
 4. The computer-implementedmethod of claim 1, wherein a previous AV-side of the actor comprises atleast one of: ahead-of-AV; behind-AV; unknown; or undetermined.
 5. Thecomputer-implemented method of claim 1, further comprising: iteratingover time intervals to propagate information to each interval along thetrajectory; and generating an AV-side of the actor at each time intervalbased on a distance margin, and one or more parameters for scoringlongitudinal margins that can change based on the AV-side of the actor.6. The computer-implemented method of claim 5, further comprising:identifying whether a spatial gap exists including at least one timeinterval since a previous time interval which, when traversed by theactor was not included in a reference path of the AV, and predicting,based on the time interval, whether the AV is predicted to move throughthe actor, or alternatively, beside or around the actor, based on when adistance interval switches from ahead of to behind the AV, oralternatively, behind the AV to ahead of the AV, the method comprising:setting a previous AV-side of the actor to be unknown if a spatial gaphas been identified and there has not been a collision; setting theAV-side of the actor to be unaltered at a time interval based on alongitudinal distance margin and detecting a collision when alongitudinal distance margin is equal to zero; detecting a collision hasoccurred when a current AV-side of the actor has been altered and a gapin the interval is not found; and setting a current AV-side of the actoras the previous AV-side of the actor when a collision has occurred. 7.The computer-implemented method of claim 1, further comprising abackward propagation of a plurality of longitudinal margins from a firstpoint of intersection between the actor and the AV, wherein backwardpropagation is determined by iterating backward over each time intervaland setting an AV-side of the actor if it is unknown for a current timeinterval, using a previous time interval.
 8. The computer-implementedmethod of claim 1, wherein providing interaction classificationcomprises at least one of: providing scoring margins; configuring toremove uncertainty; incorporating right of way; or incorporatingcollision severity.
 9. The computer-implemented method of claim 1,wherein a probability of a collision is based on one of: 1) aprobability of a prediction adjusted to consider a right of way andreactivity, or 2) a predicted probability of compliance and an abilityto adjust course; and wherein a severity of an anticipated collision isbased on one of: 1) a relative speed of the AV and the actor, 2) anactor classification comprising one of: a vulnerable road user; avehicle; an animal; a static actor; or a background actor, or 3) a pointof impact on the AV, on the actor, or both.
 10. The computer-implementedmethod of claim 1, wherein generating continuous scores for eachcandidate trajectory is based on one or more of: a penalty configured bytransforming a margin value at each time interval into a penalty; adiscount factor to account for increasing uncertainty about interactionsthat will be performed further in the future, wherein each subsequenttime interval is offset from a start of a planning cycle; an actor-typefactor, discounting parameters per actor type, including at least one ofa vehicle, a crosswalk-following pedestrian, other pedestrian, cyclist,or other object, to apply based on interaction classifications; or asocial acceptability factor that classifies a decision taken by acandidate trajectory with respect to the margins to a plurality ofactors simultaneously.
 11. A system for maneuvering an autonomousvehicle (AV) traversing a roadway, comprising: one or more processors;and a memory that stores one or more instructions that, when executed bythe one or more processors, cause the one or more processors to: receiveforecast information associated with one or more predicted trajectoriesof one or more actors in the roadway; regenerate a distance table forthe relevant trajectory of an action, using an original distance tablewhich was previously generated for processing constraints, the relevanttrajectory being based on a forecast for one or more predictedtrajectories of the actor with the trajectory of the AV; generate aplurality of margins for the AV to evaluate, the plurality of marginsbeing based on a plurality of margin types for providing informationabout risks and effects on passenger comfort associated with a futureproximity of the AV to the actor; generate continuous scores for eachcandidate trajectory that is also within the margin of the actorgenerated for the relevant trajectory; and issue a command to controlthe AV based on the continuous scores, to maneuver with respect to aplurality of constraints in the roadway.
 12. The system of claim 11,wherein the plurality of margins include at least one of a longitudinalmargin or a lateral margin, and the one or more processors are furtherconfigured to: generate a temporal longitudinal margin associated with arelevant time interval based on time remaining until the AV reaches theactor, generating the temporal longitudinal margin includes for therelevant time interval, determining a position of the AV on a referencepath and the nearest time interval where a location of a longitudinalinterval of the actor overlaps with that of the position; and generate aspatial longitudinal margin providing information for determiningproximity of the actor when moving at low speeds, where the temporallongitudinal margins may be large despite the AV having a small spatialmargin to the actor, and generating the spatial longitudinal margin,includes for each relevant time interval, further comprising: generatinglateral distance table entries associated with the actor; generating alocation interval of the reference path over which the actor is deemedwithin a threshold of the candidate trajectory and considered in thereference path of the AV; and generating a lateral margin distance asthe minimum lateral offset value over a longitudinal location intervalof the AV for the time interval being evaluated.
 13. The system of claim12, wherein the one or more processors are further configured toclassify an interaction between the AV and the actor to: perform, inresponse to predicting a collision, an interaction classification toidentify a time step associated with an instance of the collision,wherein the interaction classification identifies the time step in atime sequence of a collision time interval that a first polygonintersection is found, wherein the first polygon intersection identifiesthe collision time interval based on a first longitudinal margin of aplurality of longitudinal margins that indicates a collision; andotherwise, for each time interval, perform an interaction classificationto identify a spatial longitudinal margin accounted to be the minimumspatial longitudinal margin in the location interval of the referencepath when the actor is predicted to be the least distance from thecandidate trajectory of the AV's path, or alternatively, identify thetime interval over the longitudinal location interval of the actor whenit is predicted to have a minimum lateral offset value from thecandidate trajectory of the AV.
 14. The system of claim 11, wherein theone or more processors are further configured to: identify whether aspatial gap exists including at least one time interval since a previoustime interval which, when traversed by the actor, was not included in areference path of the AV, and predicting, based on the time interval,whether the AV is predicted to move through the actor, or alternatively,beside or around the actor, based on when a distance interval switchesfrom ahead of the AV to behind the AV or, alternatively, behind the AVto ahead of the AV, the system further comprising at least one of:setting a previous AV-side of the actor to be unknown if a spatial gaphas been identified and there has not been a collision; setting theAV-side of the actor to be unaltered at a time interval based on alongitudinal distance margin and detecting a collision when alongitudinal distance margin is equal to zero; detecting a collision hasoccurred when a current AV-side of the actor has been altered and a gapin the interval is not found; or setting a current AV-side of the actoras the previous AV-side of the actor when a collision has occurred. 15.The system of claim 14, wherein the one or more processors are furtherconfigured to backward propagate a plurality of longitudinal marginsfrom a first point of intersection between the actor and the AV, whereinbackward propagation is determined by iterating backward over each timeinterval and setting an AV-side of the actor if it is unknown for acurrent time interval, using a previous time interval.
 16. The system ofclaim 11, wherein providing interaction classification comprisesproviding scoring margins, configured to remove uncertainty, incorporateright of way, or incorporate collision severity.
 17. The system of claim11, wherein a probability of a collision is based on one of: 1) aprobability of a prediction adjusted to consider a right of way andreactivity, or 2) a predicted probability of compliance and an abilityto adjust course; and wherein a severity of an anticipated collision isbased on one of: 1) a relative speed of the AV and the actor, 2) anactor classification comprising one of: a vulnerable road user; avehicle; an animal; a static actor; or a background actor, or 3) a pointof impact on the AV, on the actor, or both.
 18. The system of claim 11,wherein the scores for each candidate trajectory, are based on one ormore of: a penalty configured by transforming a margin value at eachtime interval into a penalty; a discount factor to account forincreasing uncertainty about interactions that will be performed furtherin the future, wherein each subsequent time interval is offset from astart of a planning cycle; an actor-type factor, discounting parametersper actor type, including at least one of a vehicle, acrosswalk-following pedestrian, other pedestrian, cyclist, or otherobject, to apply based on interaction classifications; or a socialacceptability factor that classifies a decision taken by a candidatetrajectory with respect to the margins to a plurality of actorssimultaneously.
 19. A computer program product for maneuvering anautonomous vehicle (AV) traversing a route on a roadway, comprising atleast one non-transitory computer-readable medium including one or moreinstructions that, when executed by one or more processors, cause theone or more processors to: receive forecast information associated withone or more predicted trajectories of one or more actors in a roadway;regenerate a distance table for the relevant trajectory of an action,using an original distance table which was previously generated forprocessing constraints, the relevant trajectory being based on aforecast for one or more predicted trajectories of the actor with thetrajectory of the AV; generate a plurality of margins for the AV toevaluate, the margins based on a plurality of margin types for providinginformation about risks and effects on passenger comfort associated witha future proximity of the AV to the actor; generate continuous scoresfor each candidate trajectory that is also within the margin of theactor generated for the relevant trajectory; and issue a command tocontrol the AV based on the continuous scores, to maneuver with respectto a plurality of constraints in the roadway.
 20. The computer programproduct of claim 19, wherein the plurality of margins include at leastone of a longitudinal margin or a lateral margin, and further includingone or more instructions that, when executed by the one or moreprocessors, cause the one or more processors to: generate a temporallongitudinal margin associated with a relevant time interval based ontime remaining until the AV reaches the actor, generate the temporallongitudinal margin includes for the relevant time interval, anddetermine a position of the AV on a reference path and the nearest timeinterval where a location of a longitudinal interval of the actoroverlaps with that of the position, further comprising; generating aspatial longitudinal margin providing information for determiningproximity of the actor when moving at low speeds where the temporallongitudinal margins may be large despite the AV having a small spatialmargin to the actor, generating the spatial longitudinal margin includesfor each relevant time interval; generating lateral distance tableentries of the actor and determine a location interval of the referencepath over which the actor is deemed within a threshold of the candidatetrajectory and considered in the reference path of the AV; andgenerating a lateral margin distance as the minimum lateral offset valueover a longitudinal location interval of the AV for the time intervalbeing evaluated.